{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (1.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>China</th>\n",
       "      <th>China, Hong Kong SAR</th>\n",
       "      <th>China, Macao SAR</th>\n",
       "      <th>Unnamed: 4</th>\n",
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       "      <td>4</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>1976</td>\n",
       "      <td>2019</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1977</td>\n",
       "      <td>2019</td>\n",
       "      <td>688963.0</td>\n",
       "      <td>221717.0</td>\n",
       "      <td>6100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1978</td>\n",
       "      <td>2019</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1979</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1980</td>\n",
       "      <td>2019</td>\n",
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       "      <td>268803.0</td>\n",
       "      <td>13572.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1981 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year      China  China, Hong Kong SAR  China, Macao SAR  Unnamed: 4  \\\n",
       "0     1990  4231648.0              551080.0           95648.0         NaN   \n",
       "1     1990        0.0                   0.0               0.0         NaN   \n",
       "2     1990  1460345.0              516749.0           14382.0         NaN   \n",
       "3     1990  2771303.0               34331.0           81266.0         NaN   \n",
       "4     1990   245923.0                  54.0               0.0         NaN   \n",
       "...    ...        ...                   ...               ...         ...   \n",
       "1976  2019      353.0                   NaN               NaN         NaN   \n",
       "1977  2019   688963.0              221717.0            6100.0         NaN   \n",
       "1978  2019        9.0                   NaN               NaN         NaN   \n",
       "1979  2019        NaN                   NaN               NaN         NaN   \n",
       "1980  2019  2899267.0              268803.0           13572.0         NaN   \n",
       "\n",
       "      Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  \\\n",
       "0            NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "1            NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "2            NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "3            NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "4            NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "...          ...         ...         ...         ...         ...          ...   \n",
       "1976         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "1977         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "1978         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "1979         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "1980         NaN         NaN         NaN         NaN         NaN          NaN   \n",
       "\n",
       "      Unnamed: 11  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "1976          NaN  \n",
       "1977          NaN  \n",
       "1978          NaN  \n",
       "1979          NaN  \n",
       "1980          NaN  \n",
       "\n",
       "[1981 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('test.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>353.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1977</td>\n",
       "      <td>2019</td>\n",
       "      <td>688963.0</td>\n",
       "      <td>221717.0</td>\n",
       "      <td>6100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1978</td>\n",
       "      <td>2019</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1979</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>2019</td>\n",
       "      <td>2899267.0</td>\n",
       "      <td>268803.0</td>\n",
       "      <td>13572.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1981 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year      China  China, Hong Kong SAR  China, Macao SAR\n",
       "0     1990  4231648.0              551080.0           95648.0\n",
       "1     1990        0.0                   0.0               0.0\n",
       "2     1990  1460345.0              516749.0           14382.0\n",
       "3     1990  2771303.0               34331.0           81266.0\n",
       "4     1990   245923.0                  54.0               0.0\n",
       "...    ...        ...                   ...               ...\n",
       "1976  2019      353.0                   NaN               NaN\n",
       "1977  2019   688963.0              221717.0            6100.0\n",
       "1978  2019        9.0                   NaN               NaN\n",
       "1979  2019        NaN                   NaN               NaN\n",
       "1980  2019  2899267.0              268803.0           13572.0\n",
       "\n",
       "[1981 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['Year', 'China', 'China, Hong Kong SAR', 'China, Macao SAR']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>China, Macao SAR</th>\n",
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       "      <td>1990</td>\n",
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       "      <td>3975272.0</td>\n",
       "      <td>764044.0</td>\n",
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       "    <tr>\n",
       "      <td>1995</td>\n",
       "      <td>39300421.0</td>\n",
       "      <td>4471336.0</td>\n",
       "      <td>782657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>45465579.0</td>\n",
       "      <td>4978284.0</td>\n",
       "      <td>810039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>56212423.0</td>\n",
       "      <td>6033144.0</td>\n",
       "      <td>923198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>66883744.0</td>\n",
       "      <td>7256269.0</td>\n",
       "      <td>1048473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>77429957.0</td>\n",
       "      <td>8059559.0</td>\n",
       "      <td>1131367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019</td>\n",
       "      <td>81547706.0</td>\n",
       "      <td>8572027.0</td>\n",
       "      <td>1298248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           China  China, Hong Kong SAR  China, Macao SAR\n",
       "Year                                                    \n",
       "1990  33429457.0             3975272.0          764044.0\n",
       "1995  39300421.0             4471336.0          782657.0\n",
       "2000  45465579.0             4978284.0          810039.0\n",
       "2005  56212423.0             6033144.0          923198.0\n",
       "2010  66883744.0             7256269.0         1048473.0\n",
       "2015  77429957.0             8059559.0         1131367.0\n",
       "2019  81547706.0             8572027.0         1298248.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['China', 'China, Hong Kong SAR', 'China, Macao SAR' ]].groupby(df['Year']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>China, Macao SAR</th>\n",
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       "      <td>1990</td>\n",
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       "    <tr>\n",
       "      <td>2019</td>\n",
       "      <td>81547706.0</td>\n",
       "      <td>8572027.0</td>\n",
       "      <td>1298248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           China  China, Hong Kong SAR  China, Macao SAR\n",
       "Year                                                    \n",
       "1990  33429457.0             3975272.0          764044.0\n",
       "1995  39300421.0             4471336.0          782657.0\n",
       "2000  45465579.0             4978284.0          810039.0\n",
       "2005  56212423.0             6033144.0          923198.0\n",
       "2010  66883744.0             7256269.0         1048473.0\n",
       "2015  77429957.0             8059559.0         1131367.0\n",
       "2019  81547706.0             8572027.0         1298248.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['China', 'China, Hong Kong SAR', 'China, Macao SAR' ]].groupby(df['Year']).sum()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>China</th>\n",
       "      <th>China, Hong Kong SAR</th>\n",
       "      <th>China, Macao SAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1990</td>\n",
       "      <td>33429457.0</td>\n",
       "      <td>3975272.0</td>\n",
       "      <td>764044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1995</td>\n",
       "      <td>39300421.0</td>\n",
       "      <td>4471336.0</td>\n",
       "      <td>782657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>45465579.0</td>\n",
       "      <td>4978284.0</td>\n",
       "      <td>810039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>56212423.0</td>\n",
       "      <td>6033144.0</td>\n",
       "      <td>923198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>66883744.0</td>\n",
       "      <td>7256269.0</td>\n",
       "      <td>1048473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>77429957.0</td>\n",
       "      <td>8059559.0</td>\n",
       "      <td>1131367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019</td>\n",
       "      <td>81547706.0</td>\n",
       "      <td>8572027.0</td>\n",
       "      <td>1298248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           China  China, Hong Kong SAR  China, Macao SAR\n",
       "1990  33429457.0             3975272.0          764044.0\n",
       "1995  39300421.0             4471336.0          782657.0\n",
       "2000  45465579.0             4978284.0          810039.0\n",
       "2005  56212423.0             6033144.0          923198.0\n",
       "2010  66883744.0             7256269.0         1048473.0\n",
       "2015  77429957.0             8059559.0         1131367.0\n",
       "2019  81547706.0             8572027.0         1298248.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.name = None\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['China', 'China, Hong Kong SAR', 'China, Macao SAR'], dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>China</th>\n",
       "      <th>China, Hong Kong SAR</th>\n",
       "      <th>China, Macao SAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1990</td>\n",
       "      <td>33429457.0</td>\n",
       "      <td>3975272.0</td>\n",
       "      <td>764044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1995</td>\n",
       "      <td>39300421.0</td>\n",
       "      <td>4471336.0</td>\n",
       "      <td>782657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>45465579.0</td>\n",
       "      <td>4978284.0</td>\n",
       "      <td>810039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>56212423.0</td>\n",
       "      <td>6033144.0</td>\n",
       "      <td>923198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>66883744.0</td>\n",
       "      <td>7256269.0</td>\n",
       "      <td>1048473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>77429957.0</td>\n",
       "      <td>8059559.0</td>\n",
       "      <td>1131367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019</td>\n",
       "      <td>81547706.0</td>\n",
       "      <td>8572027.0</td>\n",
       "      <td>1298248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           China  China, Hong Kong SAR  China, Macao SAR\n",
       "1990  33429457.0             3975272.0          764044.0\n",
       "1995  39300421.0             4471336.0          782657.0\n",
       "2000  45465579.0             4978284.0          810039.0\n",
       "2005  56212423.0             6033144.0          923198.0\n",
       "2010  66883744.0             7256269.0         1048473.0\n",
       "2015  77429957.0             8059559.0         1131367.0\n",
       "2019  81547706.0             8572027.0         1298248.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['China', 'China, Hong Kong SAR', 'China, Macao SAR' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['China', 'HongKong', 'Macao'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'China':'China', 'China, Hong Kong SAR':'HongKong', 'China, Macao SAR':'Macao'}, inplace=True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>China</th>\n",
       "      <th>HongKong</th>\n",
       "      <th>Macao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1990</td>\n",
       "      <td>33429457.0</td>\n",
       "      <td>3975272.0</td>\n",
       "      <td>764044.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1995</td>\n",
       "      <td>39300421.0</td>\n",
       "      <td>4471336.0</td>\n",
       "      <td>782657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>45465579.0</td>\n",
       "      <td>4978284.0</td>\n",
       "      <td>810039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2005</td>\n",
       "      <td>56212423.0</td>\n",
       "      <td>6033144.0</td>\n",
       "      <td>923198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010</td>\n",
       "      <td>66883744.0</td>\n",
       "      <td>7256269.0</td>\n",
       "      <td>1048473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015</td>\n",
       "      <td>77429957.0</td>\n",
       "      <td>8059559.0</td>\n",
       "      <td>1131367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019</td>\n",
       "      <td>81547706.0</td>\n",
       "      <td>8572027.0</td>\n",
       "      <td>1298248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           China   HongKong      Macao\n",
       "1990  33429457.0  3975272.0   764044.0\n",
       "1995  39300421.0  4471336.0   782657.0\n",
       "2000  45465579.0  4978284.0   810039.0\n",
       "2005  56212423.0  6033144.0   923198.0\n",
       "2010  66883744.0  7256269.0  1048473.0\n",
       "2015  77429957.0  8059559.0  1131367.0\n",
       "2019  81547706.0  8572027.0  1298248.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "China       float64\n",
       "HongKong    float64\n",
       "Macao       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99c95418d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.index = df.index.map(int) \n",
    "df.plot(kind='line', figsize=(10, 6))\n",
    "\n",
    "plt.title('Immigration')\n",
    "plt.ylabel('Number of Immigrants')\n",
    "plt.xlabel('Years')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
